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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import numpy as np |
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class Dist(nn.Module): |
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def __init__(self, num_classes=10, num_centers=1, feat_dim=2, init='random'): |
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super(Dist, self).__init__() |
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self.feat_dim = feat_dim |
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self.num_classes = num_classes |
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self.num_centers = num_centers |
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if init == 'random': |
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self.centers = nn.Parameter(0.1 * torch.randn(num_classes * num_centers, self.feat_dim)) |
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else: |
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self.centers = nn.Parameter(torch.Tensor(num_classes * num_centers, self.feat_dim)) |
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self.centers.data.fill_(0) |
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def forward(self, features, center=None, metric='l2'): |
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if metric == 'l2': |
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f_2 = torch.sum(torch.pow(features, 2), dim=1, keepdim=True) |
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if center is None: |
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c_2 = torch.sum(torch.pow(self.centers, 2), dim=1, keepdim=True) |
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dist = f_2 - 2*torch.matmul(features, torch.transpose(self.centers, 1, 0)) + torch.transpose(c_2, 1, 0) |
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else: |
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c_2 = torch.sum(torch.pow(center, 2), dim=1, keepdim=True) |
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dist = f_2 - 2*torch.matmul(features, torch.transpose(center, 1, 0)) + torch.transpose(c_2, 1, 0) |
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dist = dist / float(features.shape[1]) |
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else: |
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if center is None: |
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center = self.centers |
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else: |
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center = center |
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dist = features.matmul(center.t()) |
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dist = torch.reshape(dist, [-1, self.num_classes, self.num_centers]) |
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dist = 0.5 * torch.mean(dist, dim=2) |
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return dist |
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